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公开(公告)号：US20220237925A1
公开(公告)日：2022-07-28
申请号：US17718721
申请日：2022-04-12
Applicant: Nvidia Corporation
Inventor： Ishwar Kulkarni , Ibrahim Eden , Michael Kroepfl , David Nister
Abstract: LiDAR (light detection and ranging) and RADAR (radio detection and ranging) systems are commonly used to generate point cloud data for 3D space around vehicles, for such functions as localization, mapping, and tracking. This disclosure provides improved techniques for processing the point cloud data that has been collected. The improved techniques include mapping one or more point cloud data points into a depth map, the one or more point cloud data points being generated using one or more sensors; determining one or more mapped point cloud data points within a bounded area of the depth map, and detecting, using one or more processing units and for an environment surrounding a machine corresponding to the one or more sensors, a location of one or more entities based on the one or more mapped point cloud data points.
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2.发明申请公开(公告)号：US20210295171A1
公开(公告)日：2021-09-23
申请号：US16824199
申请日：2020-03-19
Applicant: NVIDIA Corporation
Inventor： Alexey Kamenev , Nikolai Smolyanskiy , Ishwar Kulkarni , Ollin Boer Bohan , Fangkai Yang , Alperen Degirmenci , Ruchi Bhargava , Urs Muller , David Nister , Rotem Aviv
Abstract: In various examples, past location information corresponding to actors in an environment and map information may be applied to a deep neural network (DNN)—such as a recurrent neural network (RNN)—trained to compute information corresponding to future trajectories of the actors. The output of the DNN may include, for each future time slice the DNN is trained to predict, a confidence map representing a confidence for each pixel that an actor is present and a vector field representing locations of actors in confidence maps for prior time slices. The vector fields may thus be used to track an object through confidence maps for each future time slice to generate a predicted future trajectory for each actor. The predicted future trajectories, in addition to tracked past trajectories, may be used to generate full trajectories for the actors that may aid an ego-vehicle in navigating the environment.
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公开(公告)号：US11301697B2
公开(公告)日：2022-04-12
申请号：US16938473
申请日：2020-07-24
Applicant: Nvidia Corporation
Inventor： Ishwar Kulkarni , Ibrahim Eden , Michael Kroepfl , David Nister
Abstract: Various types of systems or technologies can be used to collect data in a 3D space. For example, LiDAR (light detection and ranging) and RADAR (radio detection and ranging) systems are commonly used to generate point cloud data for 3D space around vehicles, for such functions as localization, mapping, and tracking. This disclosure provides improved techniques for processing the point cloud data that has been collected. The improved techniques include mapping 3D point cloud data points into a 2D depth map, fetching a group of the mapped 3D point cloud data points that are within a bounded window of the 2D depth map; and generating geometric space parameters based on the group of the mapped 3D point cloud data points. The generated geometric space parameters may be used for object motion, obstacle detection, freespace detection, and/or landmark detection for an area surrounding a vehicle.
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公开(公告)号：US10776983B2
公开(公告)日：2020-09-15
申请号：US16051263
申请日：2018-07-31
Applicant: Nvidia Corporation
Inventor： Ishwar Kulkarni , Ibrahim Eden , Michael Kroepfl , David Nister
IPC: G01S17/58 , G01S17/89 , G01S17/931 , G01K9/00 , G06T11/00 , G06T15/04 , G06T7/20 , G06T7/30 , G06T7/521 , G06K9/00
Abstract: Various types of systems or technologies can be used to collect data in a 3D space. For example, LiDAR (light detection and ranging) and RADAR (radio detection and ranging) systems are commonly used to generate point cloud data for 3D space around vehicles, for such functions as localization, mapping, and tracking. This disclosure provides improvements for processing the point cloud data that has been collected. The processing improvements include analyzing point cloud data using trajectory equations, depth maps, and texture maps. The processing improvements also include representing the point cloud data by a two dimensional depth map or a texture map and using the depth map or texture map to provide object motion, obstacle detection, freespace detection, and landmark detection for an area surrounding a vehicle.
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公开(公告)号：US12001958B2
公开(公告)日：2024-06-04
申请号：US16824199
申请日：2020-03-19
Applicant: NVIDIA Corporation
Inventor： Alexey Kamenev , Nikolai Smolyanskiy , Ishwar Kulkarni , Ollin Boer Bohan , Fangkai Yang , Alperen Degirmenci , Ruchi Bhargava , Urs Muller , David Nister , Rotem Aviv
Abstract: In various examples, past location information corresponding to actors in an environment and map information may be applied to a deep neural network (DNN)—such as a recurrent neural network (RNN)—trained to compute information corresponding to future trajectories of the actors. The output of the DNN may include, for each future time slice the DNN is trained to predict, a confidence map representing a confidence for each pixel that an actor is present and a vector field representing locations of actors in confidence maps for prior time slices. The vector fields may thus be used to track an object through confidence maps for each future time slice to generate a predicted future trajectory for each actor. The predicted future trajectories, in addition to tracked past trajectories, may be used to generate full trajectories for the actors that may aid an ego-vehicle in navigating the environment.
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Patent Agency Ranking
公开(公告)号：US20220138568A1
公开(公告)日：2022-05-05
申请号：US17453055
申请日：2021-11-01
Applicant: NVIDIA Corporation
Inventor： Nikolai Smolyanskiy , Alexey Kamenev , Lirui Wang , David Nister , Ollin Boer Bohan , Ishwar Kulkarni , Fangkai Yang , Julia Ng , Alperen Degirmenci , Ruchi Bhargava , Rotem Aviv
Abstract: In various examples, reinforcement learning is used to train at least one machine learning model (MLM) to control a vehicle by leveraging a deep neural network (DNN) trained on real-world data by using imitation learning to predict movements of one or more actors to define a world model. The DNN may be trained from real-world data to predict attributes of actors, such as locations and/or movements, from input attributes. The predictions may define states of the environment in a simulator, and one or more attributes of one or more actors input into the DNN may be modified or controlled by the simulator to simulate conditions that may otherwise be unfeasible. The MLM(s) may leverage predictions made by the DNN to predict one or more actions for the vehicle.
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公开(公告)号：US20190266736A1
公开(公告)日：2019-08-29
申请号：US16051263
申请日：2018-07-31
Applicant: Nvidia Corporation
Inventor： Ishwar Kulkarni , Ibrahim Eden , Michael Kroepfl , David Nister
Abstract: Various types of systems or technologies can be used to collect data in a 3D space. For example, LiDAR (light detection and ranging) and RADAR (radio detection and ranging) systems are commonly used to generate point cloud data for 3D space around vehicles, for such functions as localization, mapping, and tracking. This disclosure provides improvements for processing the point cloud data that has been collected. The processing improvements include analyzing point cloud data using trajectory equations, depth maps, and texture maps. The processing improvements also include representing the point cloud data by a two dimensional depth map or a texture map and using the depth map or texture map to provide object motion, obstacle detection, freespace detection, and landmark detection for an area surrounding a vehicle.
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公开(公告)号：US11908203B2
公开(公告)日：2024-02-20
申请号：US17718721
申请日：2022-04-12
Applicant: Nvidia Corporation
Inventor： Ishwar Kulkarni , Ibrahim Eden , Michael Kroepfl , David Nister
CPC classification number: G06V20/58 , G01S17/89 , G06T7/30 , G06T7/521 , G06T11/001 , G06T15/04 , G06V20/56 , G06V20/582 , G06V20/584 , G06T2207/10028 , G06T2207/30241
Abstract: LiDAR (light detection and ranging) and RADAR (radio detection and ranging) systems are commonly used to generate point cloud data for 3D space around vehicles, for such functions as localization, mapping, and tracking. Improved techniques for processing the point cloud data that has been collected are provided. The improved techniques include mapping one or more point cloud data points into a depth map, the one or more point cloud data points being generated using one or more sensors; determining one or more mapped point cloud data points within a bounded area of the depth map, and detecting, using one or more processing units and for an environment surrounding a machine corresponding to the one or more sensors, a location of one or more entities based on the one or more mapped point cloud data points.
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公开(公告)号：US10769840B2
公开(公告)日：2020-09-08
申请号：US16051219
申请日：2018-07-31
Applicant: Nvidia Corporation
Inventor： Ishwar Kulkarni , Ibrahim Eden , Michael Kroepfl , David Nister
Abstract: Various types of systems or technologies can be used to collect data in a 3D space. For example, LiDAR (light detection and ranging) and RADAR (radio detection and ranging) systems are commonly used to generate point cloud data for 3D space around vehicles, for such functions as localization, mapping, and tracking. This disclosure provides improvements for processing the point cloud data that has been collected. The processing improvements include using a three dimensional polar depth map to assist in performing nearest neighbor analysis on point cloud data for object detection, trajectory detection, freespace detection, obstacle detection, landmark detection, and providing other geometric space parameters.
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公开(公告)号：US20190266779A1
公开(公告)日：2019-08-29
申请号：US16051219
申请日：2018-07-31
Applicant: Nvidia Corporation
Inventor： Ishwar Kulkarni , Ibrahim Eden , Michael Kroepfl , David Nister
Abstract: Various types of systems or technologies can be used to collect data in a 3D space. For example, LiDAR (light detection and ranging) and RADAR (radio detection and ranging) systems are commonly used to generate point cloud data for 3D space around vehicles, for such functions as localization, mapping, and tracking. This disclosure provides improvements for processing the point cloud data that has been collected. The processing improvements include using a three dimensional polar depth map to assist in performing nearest neighbor analysis on point cloud data for object detection, trajectory detection, freespace detection, obstacle detection, landmark detection, and providing other geometric space parameters.
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